Central Banking Publications have designed this two-day training course to provide practitioners with the latest developments and good practice methods regarding Basel III and CRD IV and implementingâ¦

Central Banking Publications have designed this two-day training course to provide practitioners with the latest developments and good practice methods regarding Basel III and CRD IV and implementingâ¦

Over the past few years, banks have begun to investigate blockchain, open banking has entered the scene and cyber security has climbed the agenda. But, perhaps most importantly, artificial intelligence (AI) has been reshaping how the private and public sectors go about their work, reducing man hours and cutting costs.

Central banks are no exception. The board of the Bank of England’s (BoE’s) Prudential Regulation Authority (PRA) tasked its data innovation team with investigating whether it could take advantage of machine learning capabilities to explore forward-looking concepts in relation to regtech.

Currently, the BoE collects a huge array of data from the firms it regulates – in terms of volume, the central bank collects the equivalent of the entire works of Shakespeare twice every week. The quantity of unstructured data collected by various teams means it is challenging for the supervisor to analyse all of the data comprehensively and consistently.

AI has already been put to work boosting the BoE’s analytical capabilities. One example is its proof of concept (PoC) with the iManage RAVNACE engine, an AI platform used to extract key data from unstructured and complex data tables.

The BoE’s latest PoC, however, has paved the way for robotic process automation within the PRA, proving that augmenting machine learning capabilities can enhance complex processes, allowing the central bank to implement a forward-looking approach to regtech. This latest experiment involved searching through large volumes of textual and unstructured information.

The unsupervised machine learning system clustered similar documents for easy auto-tagging and classification; duplicates and similar documents could then be identified and grouped accordingly. After grouping, the program allowed the central bank to rapidly analyse and process the data based on key areas mentioned by firms as supervisory priorities.

“Data extraction and graphical representation of key metrics along with time series allowed supervisors to quickly determine a shift in direction,” says Sholthana Begum, senior technical specialist at the BoE.

The experiment has allowed the BoE to better analyse trends within datasets, both past and present. For example, the bank is now able to conduct a peer analysis on public earnings of large banks in the UK, giving insight into growing trends in the sector. These trends, Begum says, can be translated into simple graphs, providing the regulator with a visual representation of changes in the country’s financial ecosystem.

The central bank has overcome the risks of applying innovative, forward-thinking regtech. But while the project has reaped early success, the PoC has been valuable in teaching the BoE about the limitations of machine learning within its current framework. Moving forward, adaptations can be made to further enhance supervisors’ AI capabilities.